A system and method is provided that ranks and sorts websites, apps, email, or VR environment content in real-time to increase engagement, CTR, conversions, and revenue. A client applies attributes to sections of the digital content. A server system tracks end user inputs and generates optimized layouts for the digital content, such as a webpage. The document layout is ordered or reorganized before or after the document is delivered to the end user.
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2. The system of claim 1 wherein the content sort service further provides a response to an end user, where the response comprises the optimized order for the plurality of sections of the digital content.
This invention relates to a system for optimizing the presentation order of digital content sections to enhance user engagement. The system addresses the challenge of determining the most effective sequence for displaying content sections to maximize user interaction, such as reading time or completion rates. The system includes a content sort service that analyzes user behavior data, such as interaction patterns with similar content, to predict the optimal order for presenting sections. The service processes this data to generate a ranked sequence of sections, ensuring that the most engaging or relevant content is prioritized. The system also provides a response to the end user, delivering the optimized order for the content sections. This response may be integrated into a content delivery platform, such as a web or mobile application, to dynamically adjust the presentation of content based on real-time or historical user data. The system may further include a data collection module to gather user interaction metrics, such as time spent on each section or navigation patterns, which are used to refine the sorting algorithm. The content sort service may employ machine learning techniques to continuously improve the accuracy of the optimized order based on evolving user preferences and behavior. The overall goal is to enhance user experience by tailoring content presentation to individual or group engagement patterns.
3. The system of claim 1, wherein the content sort service further provides a response to a client server, where the response comprises the optimized order for the plurality of sections for the digital content.
A system for optimizing the presentation of digital content involves a content sort service that processes multiple sections of digital content to determine an optimized order for display. The system addresses the challenge of efficiently organizing content to enhance user engagement and readability. The content sort service analyzes the sections based on predefined criteria, such as relevance, importance, or user preferences, to generate an optimized sequence. This optimized order is then transmitted as a response to a client server, which can be a web server, application server, or other computing device responsible for delivering the content to end users. The response includes the ordered list of sections, allowing the client server to present the content in the most effective manner. The system may also incorporate additional features, such as dynamic adjustments based on real-time user interactions or contextual factors, to further refine the ordering process. By automating the sorting of content sections, the system improves the efficiency of content delivery and enhances the user experience.
4. The system of claim 1, wherein the optimization request for digital content comprises data indicating that one or more of the plurality of sections of the digital content are pinned.
The system optimizes the delivery of digital content, such as video or audio streams, by dynamically adjusting the transmission parameters based on network conditions and user preferences. The core system monitors network performance metrics like bandwidth, latency, and packet loss to select the most efficient encoding and transmission settings for each segment of the content. This ensures smooth playback and minimizes buffering or quality degradation. A key feature of this system is the ability to handle pinned sections within the digital content. Pinned sections are specific parts of the content that must be delivered with higher priority or guaranteed quality, regardless of network fluctuations. For example, in a video stream, critical scenes or advertisements may be marked as pinned to ensure they are transmitted with the highest possible quality. The system identifies these pinned sections based on metadata or user-defined settings and allocates additional resources to prioritize their delivery. This ensures that even if other parts of the content are adjusted for lower quality due to network constraints, the pinned sections remain intact and maintain their intended quality. The system dynamically adjusts the transmission strategy for both pinned and non-pinned sections to optimize overall performance while preserving the integrity of critical content.
5. The system of claim 4, wherein the pinned sections of the digital content are ignored by the content sort service.
6. The system of claim 1, wherein the optimization request for digital content additionally comprises a key performance indicator, and wherein the machine learning process uses the key performance indicator to determine how to optimize the digital content.
The system optimizes digital content delivery by analyzing user interactions and adjusting content presentation to improve performance. The system collects data on how users engage with digital content, such as click-through rates, dwell time, or conversion rates, and applies machine learning to identify patterns and predict optimal content configurations. These configurations may include layout adjustments, text variations, or multimedia elements tailored to different user segments. The system dynamically updates content based on real-time feedback to maximize engagement or other performance metrics. The optimization process is further enhanced by incorporating a key performance indicator (KPI) into the request. The KPI defines the specific goal of the optimization, such as increasing click-through rates, reducing bounce rates, or improving conversion rates. The machine learning process evaluates the KPI to determine the most effective content adjustments. For example, if the KPI is conversion rate, the system may prioritize content variations that historically lead to higher conversions. The system continuously refines its recommendations based on ongoing performance data, ensuring that the content remains aligned with the desired KPI. This approach enables automated, data-driven optimization of digital content to achieve measurable business outcomes.
7. The system of claim 1, further comprising a track service, where the track service stores end user request data in a database.
A system for managing end user requests includes a track service that stores end user request data in a database. The system is designed to handle and process requests from end users, ensuring that the data associated with these requests is recorded and maintained. The track service specifically focuses on capturing and storing request-related information, which may include details such as request timestamps, user identifiers, request parameters, and status updates. This stored data can be used for tracking, analysis, or reporting purposes, allowing the system to monitor request processing, identify trends, and improve service efficiency. The database serves as a centralized repository for this information, enabling quick retrieval and management of request data. By integrating the track service with the system, the overall functionality is enhanced, providing better visibility into request handling and ensuring that all relevant data is preserved for future reference. This approach improves system reliability and user experience by maintaining accurate records of end user interactions.
8. The system of claim 1, wherein at least one of the plurality of sections of the digital content comprises a plurality of subsections, where the content sort service further generates an optimized order for the plurality of subsections.
The invention relates to a digital content management system designed to optimize the presentation of content by dynamically sorting and ordering sections and subsections. The system addresses the challenge of efficiently organizing digital content to enhance user engagement and accessibility. The core functionality involves analyzing the structure of digital content, which is divided into multiple sections, and further subdividing at least one of these sections into multiple subsections. A content sort service within the system evaluates these subsections to determine an optimized order for presentation. This optimization may be based on factors such as relevance, user preferences, or contextual data. The system ensures that the content is displayed in a manner that maximizes clarity and usability, improving the overall user experience. The dynamic sorting capability allows for real-time adjustments, ensuring that the content remains relevant and engaging as user interactions evolve. This approach is particularly useful in applications where content structure is complex, such as educational platforms, documentation systems, or interactive media, where logical and intuitive organization is critical. The system's ability to handle nested content structures—sections within sections—enhances flexibility and adaptability across various use cases.
10. The method of claim 9, further comprising the step of resizing the sections of the digital content based on the optimized order for the plurality of sections.
A method for optimizing the presentation of digital content involves analyzing and rearranging sections of the content to improve readability, engagement, or other performance metrics. The method includes evaluating the content to determine an optimal sequence for the sections, which may involve assessing factors such as user behavior, content relevance, or structural dependencies. Once the optimal order is determined, the sections are resized to enhance the presentation. Resizing may involve adjusting the dimensions, proportions, or layout of each section to ensure coherence and visual appeal in the new arrangement. This method is particularly useful for digital documents, web pages, or multimedia content where the order and size of sections can significantly impact user experience. The resizing step ensures that the content remains visually balanced and logically structured after reordering, maintaining readability and aesthetic consistency. The technique may be applied automatically or with user input, depending on the application.
11. The method of claim 9, further comprising the step of removing one or more sections of the digital content.
A system and method for processing digital content involves analyzing the content to identify sections that may be removed without significantly affecting the overall meaning or utility of the content. The method includes detecting structural elements such as paragraphs, images, or multimedia components within the digital content. It then evaluates these elements to determine their relevance or redundancy based on predefined criteria, such as user preferences, content importance, or contextual relevance. The system may also compare sections to external data sources or user feedback to assess their necessity. Once identified, the system removes the selected sections, generating a modified version of the digital content that is more concise or tailored to specific needs. This process can be applied to text documents, web pages, multimedia files, or other forms of digital content. The method may also include preserving certain sections based on user-defined rules or metadata tags, ensuring critical information remains intact. The removal process may be reversible, allowing users to restore deleted sections if needed. The system can operate automatically or with user input, providing flexibility in how content is processed. The goal is to enhance efficiency, reduce redundancy, and improve the usability of digital content.
12. The method of claim 9, wherein at least one of the plurality of sections of the digital content comprises a plurality of subsections, where the method further comprises the step of generating an optimized order for the plurality of subsections.
This invention relates to digital content organization, specifically optimizing the presentation order of content sections and subsections to enhance user engagement or other performance metrics. The problem addressed is the inefficiency of static or manually ordered content, which may not adapt to user preferences or behavioral patterns, leading to suboptimal engagement or learning outcomes. The method involves analyzing user interactions with digital content to determine optimal presentation sequences. For sections containing multiple subsections, the method further generates an optimized order for these subsections based on user behavior data. This may include tracking metrics such as time spent, completion rates, or navigation patterns to identify the most effective sequence. The optimized order can be dynamically adjusted in real-time or precomputed for future presentations. The goal is to improve user experience, retention, or other performance indicators by tailoring content flow to individual or group preferences. The method may apply to educational materials, marketing content, or any structured digital information where presentation order impacts effectiveness.
13. The method of claim 9, further comprising the step of adding one or more attributes to one or more of the plurality of sections of the digital content.
A system and method for managing digital content involves organizing the content into multiple sections, where each section is associated with a unique identifier. The method includes generating a visual representation of the content structure, allowing users to navigate and interact with the sections. The visual representation may include a hierarchical or non-hierarchical layout, with sections displayed as nodes or other graphical elements. Users can manipulate the visual representation to modify the content structure, such as adding, removing, or rearranging sections. The method also supports adding attributes to the sections, which can include metadata, tags, or other descriptive information. These attributes enhance content organization, searchability, and retrieval. The system may further include features for collaborative editing, version control, and real-time updates. The method is applicable to various types of digital content, including documents, multimedia files, and software code, improving efficiency in content management and collaboration.
14. The method of claim 9, wherein the optimization request comprises a key performance indicator.
A system and method optimize performance in a computing environment by analyzing and adjusting system parameters based on key performance indicators (KPIs). The method involves receiving an optimization request that includes a KPI, such as latency, throughput, or resource utilization, which defines the performance metric to be improved. The system then identifies relevant system parameters that influence the KPI, such as processor allocation, memory usage, or network bandwidth. Using machine learning or statistical analysis, the system evaluates the impact of adjusting these parameters on the KPI. Based on this analysis, the system generates and applies parameter adjustments to optimize the KPI. The method may also include monitoring the system's performance after adjustments to ensure the KPI is met and iteratively refining the parameters if necessary. This approach allows for dynamic, data-driven optimization of system performance in real-time, improving efficiency and responsiveness in computing environments.
16. The system of claim 15, wherein the content sort service further provides a response to an end user, where the response comprises the optimized order for the plurality of sections for the digital content.
This invention relates to a system for optimizing the presentation order of sections within digital content to enhance user engagement. The system addresses the challenge of determining an effective sequence for content sections to maximize user interaction, such as reading time or completion rates. The system includes a content sort service that analyzes user behavior data, such as interaction patterns with similar content, to generate an optimized order for the sections. The service may also incorporate metadata about the content, such as section importance or dependencies, to refine the ordering. The optimized order is then provided to an end user, ensuring the content is presented in a way that aligns with predicted user preferences and engagement metrics. The system may further include a data collection module to gather user interaction data and a machine learning model to predict the most effective section sequence based on historical and real-time data. The invention aims to improve user experience by dynamically adapting content presentation to individual or group behavior patterns.
17. The system of claim 15, wherein the content sort service further provides a response to a client server, where the response comprises the optimized order for the plurality of sections for the digital content.
A system for optimizing the presentation of digital content involves dynamically sorting content sections based on user preferences, behavior, or other contextual factors. The system includes a content sort service that analyzes input data, such as user interactions or metadata, to determine an optimal order for displaying multiple sections of digital content. This optimized order is then provided to a client server, which receives a response containing the sorted sequence of sections. The system may also include a data processing module to prepare the input data for analysis and a user interface module to present the sorted content to end-users. The goal is to enhance user engagement and efficiency by tailoring the content structure to individual needs or contextual conditions. The system may apply machine learning or rule-based algorithms to generate the optimized order, ensuring adaptability to different types of digital content, such as articles, multimedia, or interactive elements. The response sent to the client server includes the final sorted sequence, enabling seamless integration with existing content delivery platforms. This approach improves content accessibility and relevance, addressing challenges in static or poorly organized digital presentations.
18. The system of claim 15, wherein the optimization request for digital content comprises a key performance indicator, where the content sort service additionally uses the key performance indicator to determine how to optimize the order for the plurality of sections for the digital content.
The system optimizes the presentation of digital content by dynamically sorting sections of the content to improve user engagement. The system receives an optimization request that includes a key performance indicator (KPI), such as click-through rate, time spent, or conversion rate. The content sort service analyzes this KPI to determine the optimal order for presenting the sections of the digital content. The system may also consider user behavior data, such as past interactions with similar content, to further refine the sorting. The goal is to maximize the desired outcome, such as increasing user engagement or driving specific actions. The system can adapt in real-time, adjusting the order of sections based on ongoing user interactions to continuously improve performance. This approach ensures that the most relevant or impactful sections are prioritized, enhancing the overall user experience and achieving the specified KPI. The system may also support multiple KPIs, allowing for flexible optimization strategies based on different business objectives.
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April 6, 2021
December 13, 2022
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